Flourishing by Design: Applying Self-Determination Theory and the Job Demands-Resources Model to Systems-Level Wellness in Medical Education
Bibliographic record
Abstract
Background: Physician burnout remains a defining challenge in medical education, driven by excessive demands and fragmented wellness initiatives. While calls for systemic reform grow louder, many efforts lack a unifying framework capable of addressing both distress and the cultivation of professional fulfillment. Methods: This guide applies a dual-theory lens-Self-Determination Theory (SDT) and the Job Demands-Resources (JD-R) model-to propose a systems-based approach to motivation and wellness. Drawing on empirical evidence and applied experience, it presents twelve actionable strategies across three ecological domains: the built environment, policy frameworks, and interpersonal dynamics. The first six strategies target hindrance demands that frustrate psychological needs and contribute to burnout; the next six strengthen resources that satisfy those needs and foster engagement, resilience, and well-being. Results: The strategies offer flexible, theoretically grounded entry points for reform, supporting institutions in cultivating sustainable, human-centered learning environments where wellness is embedded-not bolted on. Examples include prioritizing formative over high-stakes assessments, integrating justice and safety into institutional design, and balancing clinical responsibility with developmental support. Conclusions: Integrating SDT and JD-R provides a rigorous, coherent, and scalable foundation for systems-level wellness initiatives. It reframes well-being not as the absence of burnout but as the presence of flourishing-offering a shared language, validated metrics, and a roadmap for lasting cultural and structural transformation in medical education.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".